Technology and Health Care最新文献

Background: The optimal intervention procedures for scar-related ventricular tachycardia (VT) is still unclear.

Objective: This study aimed to compare the acute and long-term outcomes of a stepwise ablation approach targeting critical sites identified through activation mapping during VT or pace mapping followed by substrate ablation with substrate modification alone in patients with scar-related VT.

Methods: Data of 41 patients with scar-related VTs treated with stepwise ablation (Group 1, n = 29) or substrate modification alone during sinus rhythm (Group 2, n = 12) were retrospectively reviewed. The procedure acute success and long-term success during follow-up were compared.

Results: There was no statistical difference between the two groups on basic characteristics. Group 1 demonstrated shorter ablation time (P = 0.02), longer VT-free survival rates at a median follow-up of 24.0 months (P = 0.02) and a lower VT recurrence rate (hazard ratio: 0.17, 95% confidence interval: [0.03, 0.93], P = 0.04) compared to Group 2. The acute success and ratio of ablation area to scar area were comparable between the two groups (P ≥ 0.05).

Conclusion: The stepwise ablation strategy shows promise for improving acute and long-term outcomes and reducing the recurrence risk in patients with scar-related VT.

Hearing impairment, often caused by noise-induced trauma, significantly affects sound perception, communication, and cognitive abilities while increasing the risk of secondary accidents-individuals with hearing impairment are twice as likely to experience accidents as those with normal hearing. According to a 2023 WHO report, approximately 432 million adults and 34 million children globally are affected by hearing loss. While Assistive Listening Devices are commonly recommended, they are inadequate for individuals with total hearing loss. Therefore, alternatives are necessary to enhance safety and reduce accident risks. The present study introduces a hybrid deep learning model combining Very Deep Convolutional Networks (VGG16) and Residual Networks (ResNet-50) for efficient sound wave analysis and classification. Trained and validated on a comprehensive urban sound dataset, the model achieved a remarkable accuracy of 97.14%, surpassing existing state-of-the-art solutions. Furthermore, a mobile-based assistive notification system, MANSHIP, was developed to detect environmental sounds and alert individuals with profound or total hearing loss to potential hazards. MANSHIP addresses critical safety challenges and demonstrates the potential to improve the quality of life for those with severe hearing impairments by fostering safer environments and reducing caregiver dependency.

Background: Recurrent anterior shoulder dislocation is a common shoulder problem, usually caused by a force from the front when the shoulder joint is abducted and externally rotated. In the present study, we investigated the effect of arthroscopic subscapularis augmentation using the long head of the biceps tendon on shoulder motion after restoring anterior stability of the joint in patients with 13.5-20% of scapular glenoid defects.

Methods: Fifty patients admitted to our department with recurrent anterior shoulder dislocation between April 2017 and July 2021 were retrospectively analyzed. The patients were divided into two groups (groups A and B, n = 25 each) with comparable age, sex, hand dominance, and articular glenoid bone loss. Patients in group A were treated with arthroscopic Bankart repair and subscapularis augmentation, whereas those in group B underwent arthroscopic long head of the biceps transposition and subscapularis augmentation. All patients in both groups were followed up for more than 1 year, with a mean follow-up period of 20.1 ± 0.7 months (range, 13-28 months). The primary outcomes were changes in the visual analog scale score, Rowe classification, and Constant-Murley shoulder outcome score.

Results: None of the patients in either group had experienced recurrent dislocation at 1-year follow-up. The visual analog scale scores decreased, and the Rowe and Constant-Murley scores improved significantly compared to the preoperative scores. Significant differences were observed in the forward flexion, abduction, and internal rotation angles of the shoulder joint in both groups at 1-year follow-up compared to baseline. The postoperative forward flexion (P = 0.143), abduction (P = 0.778), and internal rotation angles (P = 0.609) did not differ significantly between the two groups. At 1-year follow-up, the loss of angles of external rotation at the side and external rotation at 90° abduction in group B exhibited significantly less angular loss than group A.

Conclusion: Arthroscopic subscapularis augmentation using the long head of the biceps transposition technique was effective at restoring anterior stability in patients with 13.5-20% scapular glenoid defects. It was more effective at restoring the external rotational function of the shoulder joint than arthroscopic Bankart repair and subscapularis augmentation.

Motion sickness induced by autonomous driving technology poses a new challenge to the emerging sustainable transportation systems. This study investigates the association between motion sickness in autonomous driving and electroencephalogram (EEG) signals under three laboratory-based simulated scenarios: manual driving, resting, and autonomous driving. EEG data were recorded from participants in each mode, alongside the collection of motion sickness symptoms through questionnaires. Data analysis and exploration were conducted to explore the relationship between autonomous driving-induced motion sickness and EEG signals. The results indicate a significantly higher probability of motion sickness among passengers in autonomous driving mode than in manual one. Across different driving modes, a correlation was observed between the amplitude and latency of N200 and P300 event-related potentials (ERPs) in the Go/Nogo paradigm, reflecting response inhibition and the occurrence of motion sickness. Temporal analysis of EEG signals revealed significant differences in the Kolmogorov complexity values at Cz, Fz, and Pz channels, suggesting the potential use of EEG-based detection of motion sickness. Frequency domain analysis indicated increased activity in alpha and gamma waves and decreased activity in beta waves following the onset of motion sickness during autonomous driving. Distinct changes were observed in the electrocortical topography of N200 and P300 components in autonomous driving through event-related potential waveforms and topographic maps. These findings provide new insights into the neural mechanisms of motion sickness in autonomous driving and offer guidance for future intervention methods and improvements in the design of autonomous driving systems, thereby promoting their sustainability and safety.

Segmenting anterior and posterior cruciate ligaments (ACL/PCL) presents challenges in medical imaging due to diverse characteristics, including size, shape, and intensity. Our study uses superpixel-based spectral clustering for knee cruciate ligament segmentation in 2D DICOM slices, renowned for generating high-quality clusters. The proposed method addresses the challenges by (i) identifying the ligamentous region (ROI) through superpixel-based computation, (ii) extracting features (intensity-based, shape-based, geometric complexity, and Scale-Invariant Feature Transform) from the ROI, and (iii) segmenting knee ligament tissues using spectral clustering on the extracted features. Superpixel-based spectral clustering addresses the challenge of constructing a dense similarity matrix and significantly reduces the computational burden. Furthermore, 3D visualization of ligament structures is performed using the Visualization Toolkit (VTK). We evaluated our proposed approach on a dataset of knee MRI slices, assessing the results via the dice score, average surface distance (ASD), and root mean squared error (RMSE) metrics. Our method achieved an average dice score of 0.912 for ACL segmentation and 0.896 for PCL segmentation, outperforming other clustering methods. These scores showed an enhancement of 10.7% and 14.9% in segmentation accuracy for the ACL and PCL, respectively. Furthermore, reduced error margins were demonstrated with the mean ASD values of 1.60 and 1.78 and the mean RMSE values of 1.76 and 1.86 for ACL and PCL, respectively. These results show the effectiveness of the proposed method for cruciate ligament segmentation and its potential for increasing the segmentation accuracy and speed, offering significant advantages over manual segmentation by reducing time and expertise.

Objective: Glioma is a common tumor in neurosurgery. Glut-1 is the main carrier of glucose uptake by cells and can provide energy through the glycolytic pathway. However, the role and related mechanisms of Glut-1 in glioma have not yet been elucidated.

Methods: Real time PCR and Western blot were done to assess Glut-1 level in glioma tumor tissues and adjacent tissues. Glioma U87 cells were separated into control group; Glut-1 negative control (NC group); and Glut-1 siRNA group followed by analysis of Glut-1 expression, cell proliferation by MTT assay, cell invasion, cell apoptosis and cycle by flow cytometry and AKT / mTOR signaling proteins level by Western blot.

Results: Glut-1 expression was significantly increased in the tissues of glioma patients (P < 0.05) compared to adjacent tissues and its level was related to tumor size, pathological grade and survival. Down-regulating the expression of Glut-1 can significantly inhibit tumor cell proliferation and invasion, increase apoptosis and induce G0 phase of cell cycle arrest, and inhibit the expression of AKT / mTOR signaling proteins phosphorylation (P < 0.05).

Conclusions: Glut-1 level in glioma tissues is significantly increased, which is related to the pathological features. Down-regulating Glut-1 can inhibit glioma by regulating the AKT / mTOR signaling pathway.

Background: Acute ischemic stroke (AIS) is a leading cause of disability and mortality worldwide, often resulting from embolic events. Patent foramen ovale (PFO), a common congenital heart defect, has been identified as a potential source of emboli in AIS patients, especially in cases of cryptogenic stroke where no other etiology is found.

Objective: This study aimed to investigate the relationship between AIS and PFO, and to explore the diagnostic value of right heart acoustic angiography for PFO.

Methods: A study included 148 AIS and 111 non-AIS patients with suspected PFO, using transesophageal echocardiography (TEE) as the diagnostic gold standard. Sensitivity, specificity, and accuracy of right heart acoustic angiography for PFO diagnosis were evaluated. ROC and Spearman correlation analyses assessed diagnostic value and the association between AIS and PFO.

Results: PFO was detected in 79 AIS patients and 20 non-AIS patients, with a higher detection rate in the AIS group (P < 0.05). Right heart acoustic angiography showed a diagnostic accuracy of 95.37% for PFO, with a sensitivity of 95.37% and a specificity of 97.78%. The AUC value of the ROC curve for right heart acoustic imaging was 0.975, indicating high diagnostic efficacy for PFO. PFO was positively correlated with AIS (r = 0.318, P < 0.001).

Conclusion: PFO is commonly detected in AIS patients and is positively correlated with AIS. Right heart acoustic imaging has high diagnostic efficacy for PFO and can be a valuable diagnostic tool for patients with PFO.

The aim of this paper is to present a case of infective endocarditis (IE) caused by Staphylococcus aureus (S. aureus), focusing on diagnostic and therapeutic approaches. The paper highlights the importance of 2D transesophageal echocardiography (TOE) and discusses the capabilities and advances of appliance of the 3D TOE in patient evaluation, better understanding of the diagnosis, and timely treatment. The use of this advance echocardiographic modality allows clear visualization of complex and destructive tissue lesions, such as the perforation observed and presented in this case.

Background: Deficits in concentration with social stimuli are more common in children affected by autism spectrum disorder (ASD). Developing visual attention is one of the most vital elements for detecting autism. Eye tracking technology is a potential method to identify an early autism biomarker based on children's abnormal visual patterns.

Objective: Eye tracking retinal scan path images can be generated by eyeball movement during the time of watching the screen and capture the eye projection sequences, which helps to analyze the behavior of the children. The Shi-Tomasi corner detection methodology uses open CV to identify the corners of the eye gaze movement in the images.

Methods: In the proposed ADET model, the corner detection-based vision transformer (CD-ViT) technique is utilized to diagnose autism at an early stage. Generally, the transformer model divides the input images into patches, which can be fed into the transformer encoder process. The vision transformer is fine-tuned to resolve binary classification issues once the features are extracted via remora optimization. Specifically, the vision transformer model acts as the cornerstone of the proposed work with the help of the corner detection technique. This study uses a dataset with 547 eye-tracking retinal scan path images for both autism and non-autistic children.

Results: Experimental results show that the suggested ADET frameworkachieves a better classification accuracy of 38.31%, 23.71%, 13.01%, 1.56%, 18.26%, and 44.56% than RM3ASD, MLP, SVM, CNN, SVM, and our proposed ADET methods.

Conclusions: This screening method strongly suggests that it be used to assist medical professionals in providing efficient and accurate autism detection.

Background: Diabetic foot ulcers (DFU) are a severe consequence of diabetes that, if left untreated, can lead to amputation, blindness, renal failure, and other serious complications. The high treatment expense and length of treatment for this therapeutic technique are both disadvantages. Despite the effectiveness of this strategy, a distant, cost-effective, and comfortable DFU diagnostic therapy is necessary.

Objective: This study proposed the Advanced Machine Learning Practical Method for Diabetic Foot Ulcer Classification.

Methods: This unique and cost-effective healthcare solution uses Practical Methodologies with the reinforcement learning algorithm for DFU imaging. The categorization was based on constant technological advancements, and the benefits of Machine Learning (ML) for use in DFU treatment are numerous, including enhanced clinical decision-making based on Ulcer classification and healing progress. The ML greatly impacted DFU data analysis, with categorization and risk assessment among the findings.

Results: The machine-learning technique can potentially create a paradigm shift by providing a 92.5% classification accuracy evaluation in the diabetic foot Ulcer problem. According to Clustering Scenario Analysis of Diabetic Foot Ulcer, when compared to Mild To Moderate Localized Cellulitis (Cluster 1 produces classification efficiency from 71% to 88%), Moderate To Severe Cellulitis (Cluster 2 delivers classification efficiency from 85% to 97%), Moderate To Severe Cellulitis With Ischemia (Cluster 3 produces classification efficiency from 90% to 98%), and Life-Or Limb-Threatening Infection (Cluster 4), the results were promising (Cluster 4 makes classification efficiency from 93.5% to 98.2%). The efficiency of this is Cluster 78.45 percent higher than the existing procedure.

Conclusions: The proposed Advanced Machine Learning Practical Method demonstrates significant improvements in DFU classification accuracy and efficiency, presenting a cost-effective and effective alternative to traditional diagnostic approaches.